I've found Ray Dalio's book Principles incredibly insightful. One area was around the use of technology for decision making.

Dalio first sets the stage for the relevant principles in sharing his life story. I've pulled out a few passages and emphasized things I found particularly interesting.

From very early on, whenever I took a position in the markets, I wrote down the criteria I used to make my decision. Then, when I closed out a trade, I could reflect on how well these criteria had worked. It occurred to me that if I wrote those criteria into formulas (now more fashionably called algorithms) and then ran historical data through them, I could test how well my rules would have worked in the past. ...

We tested the systems going as far back as we could, typically more than a century, in every country for which we had data, which gave me great perspective on how the economic/market machine worked through time and how to bet on it. Doing this helped educate me and led me to refine my criteria so they were timeless and universal. Once I vetted those relationships, I could run data through the systems as it flowed at us in real time and the computer could work just as my brain worked in processing it and making decisions.

The result was Bridgewater’s original interest rate, stock, currencies, and precious metals systems, which we then combined into one system for managing our portfolio of bets. Our system was like an EKG on the economy’s vital signs; as they changed, we changed ourpositions. However, rather than blindly following the computer’s recommendations, I would have the computer work in parallel with my own analysis and then compare the two. When the computer’s decision was different from mine, I would examine why. Most of the time, it was because I had overlooked something. In those cases, the computer taught me. But sometimes I would think about some new criteria my system would’ve missed, so I would then teach the computer. We helped each other. It didn’t take long before the computer, with its tremendous processing power, was much more effective than me. This was great, because it was like having a chess grandmaster helping me plot my moves, except this player operated according to a set of criteria that I understood and believed were logical, so there was no reason for us to ever fundamentally disagree.

The computer was much better than my brain in “thinking” about many things at once, and it could do it more precisely, more rapidly, and less emotionally. And, because it had such a great memory, it could do a better job of compounding my knowledge and the knowledge of the people I worked with as Bridgewater grew. ...

Of course, we always had the freedom to override the system, which we did less than 2 percent of the time—mostly to take money off the table during extraordinary events that weren’t programmed, like the World Trade Center going down on 9/11. While the computer was much better than our brains in many ways, it didn’t have the imagination, understanding, and logic that we did. That’s why our brains working with the computer made such a great partnership.

...

Over the last three decades of building these systems we have incorporated many more types of rules that direct every aspect of our trading. Now, as real-time data is released, our computers parse information from over 100 million datasets and give detailed instructions to other computers in ways that make logical sense to me. If I didn’t have these systems, I’d probably be broke or dead from the stress of trying so hard. We certainly wouldn’t have done as well in the markets as we have. As you will see later, I am now developing similar systems to help us make management decisions. I believe one of the most valuable things you can do to improve your decision making is to think through your principles for making decisions, write them out in both words and computer algorithms, back-test them if possible, and use them on a real-time basis to run in parallel with your brain’s decision making.

Dalio then articulates the specific principles in "Life Principles #5: Learn How to Make Decisions Effectively."

Life Principle 5.11: Convert your principles into algorithms and have the computer make decisions alongside you.

If you can do that, you will take the power of your decision making to a whole other level. In many cases, you will be able to test how that principle would have worked in the past or in various situations that will help you refine it, and in all cases, it will allow you to compound your understanding to a degree that would otherwise be impossible. It will also take emotion out of the equation. ...By developing a partnership with your computer alter ego in which you teach each other and each do what you do best, you will be much more powerful than if you went about your decision making alone. The computer will also be your link to great collective decision making, which is far more powerful than individual decision making, and will almost certainly advance the evolution of our species.

Life Principles 5.12: Be cautious about trusting AI without having deep understanding.

I worry about the dangers of AI in cases where users accept—or, worse, act upon—the cause-effect relationships presumed in algorithms produced by machine learning without understanding them deeply.

Before I explain why, I want to clarify my terms. “Artificial intelligence” and “machine learning” are words that are thrown around casually and often used as synonyms, even though they are quite different. I categorize what is going on in the world of computer-aided decision making under three broad types: expert systems, mimicking, and data mining (these categories are mine and not the ones in common use in the technology world).

Expert systems are what we use at Bridgewater, where designers specify criteria based on their logical understandings of a set of cause-effect relationships, and then see how different scenarios would emerge under different circumstances.

But computers can also observe patterns and apply them in their decision making without having any understanding of the logic behind them. I call such an approach “mimicking.” This can be effective when the same things happen reliably over and over again and are not subject to change, such as in a game bounded by hard-and-fast rules. But in the real world things do change, so a system can easily fall out of sync with reality.

The main thrust of machine learning in recent years has gone in the direction of data mining, in which powerful computers ingest massive amounts of data and look for patterns. While this approach is popular, it’s risky in cases when the future might be different from the past. Investment systems built on machine learning that is not accompanied by deep understanding are dangerous because when some decision rule is widely believed, it becomes widely used, which affects the price. In other words, the value of a widely known insight disappears over time. Without deep understanding, you won’t know if what happened in the past is genuinely of value and, even if it was, you will not be able to know whether or not its value has disappeared—or worse. It’s common for some decision rules to become so popular that they push the price far enough that it becomes smarter to do the opposite.

Remember that computers have no common sense. For example, a computer could easily misconstrue the fact that people wake up in the morning and then eat breakfast to indicate that waking up makes people hungry. I’d rather have fewer bets (ideally uncorrelated ones) in which I am highly confident than more bets I’m less confident in, and would consider it intolerable if I couldn’t argue the logic behind any of my decisions.A lot of people vest their blind faith in machine learning because they find it much easier than developing deep understanding. For me, that deep understanding is essential, especially for what I do.

Just after finishing Principles, I picked up Judea Pearl's book The Book of Why and found it echoed these same ideas. Judea Pearl won the Turing prize in 2011 for his contributions to artificial intelligence around probabilistic and causal reasoning. He's credited with the creation of Bayesian networks and then built on that contribution with his models of causality. A basic message of the book is that statistics focused to a fault on correlation over causality, even while proclaiming "correlation is not causation." This led to a focus on "big data" and "data science," which can...

...tell you that the people who took a medicine recovered faster than those who did not take it, but they can't tell you why. Maybe those who took the medicine did so because they could afford it and would have recovered just as fast without it.

To address this gap, Pearl offers an "inference engine" that combines data with causal knowledge to produce answers to queries of interest:

You can find the introductory chapter of The Book of Whyhere on Pearl's website. (The model above is on p. 12.)

This got my attention because the approach echoed Dalio's approach of systematizing principles and feeding them to the computer. However, what's lacking in the model above and different than Dalio's approach is the return path to Knowledge, or as Dalio says: "developing a partnership with your computer alter ego in which you teach each other and each do what you do best." So I added a line to the inference engine, making it an inference and teaching engine, more of a partnership:

I'm early in my learning, exploration, and curiosity mode here. I'm curious about the extent of usage and effectiveness of this approach. Is Judea Pearl something significant and new here? Is this approach widely used? Is it effective? Can it be effective? Has something significant been overlooked here in the excitement around deep learning?

I suspect the approach is not widely used. What appears to be far more common are the "black box" approaches Dalio mentions—algorithmic trading, high frequency trading, search algorithms, recommendation algorithms. To be clear, these are valuable and revolutionary. AlphaGo was an incredible success (quickly surpassed by AlphaGo Zero). But I suspect that the wild success of these approaches is masking another equally impactful, albeit less flashy approach, of improved decision making—a partnership between man and machine.

I started reading Paul Allen's memoir, Idea Man, and in setting the stage for the founding of Microsoft, Allen talks about monitoring the release of Intel's 4004 chip in November 1971 and then the much more powerful Intel 8008 four months later.

My really big ideas have all begun with a stage-setting development—in this case, the evolution of Intel's early microprocessor chips. Then I ask a few basic questions: Where is the leading edge of discovery headed? What should existing but doesn't yet? How can I help create something to meet the need, and who might be enlisted to join the cause?

This is clearly an effective approach...

Consider Reed Hastings and Netflix. The below is from A16Z podcast "Tech and Entertainment in the 'Era of Mass Customization.'" Marc Andreessen recalls how Reed Hastings had invited him to discuss streaming video in 2003-2004, and Andreessen told him, "Eh, it'll never work...there are too many technical impediments." Andreessen asked Hastings to walk through how he had thought about it, and here's what Hastings said:

On bandwidth trend. If you looked at median residential bandwidth from 1980 forward, it's completely smooth. We've gone through multiple technologies: dial-up, DSL, cable, fiber. Interestingly, the speeds at which streaming was going to become practical (1-2 Mbps) was completely knowable. Now, we weren't that smart. We knew it was sometime in the future. But—right from the beginning in 1997 we envisioned it. When I was at Stanford you take the classic Tannenbaum computer networking class, and it makes you think about networks differently. You have to calculate the bandwidth of a station wagon filled with backup tapes driving across the country. So you start to think about networks differently. So in 1997 when a friend told me about DVD, I thought, "Oh my god, that's the station wagon." That's this 5 GB packet that you could mail. (High throughput, high latency—24 hour latency but good throughput.) It's that cross-fertilization of metaphor. So we always viewed DVD by mail as a digital distribution network, and we knew that eventually [we would be delivering over the internet]. And that's why we called the company Netflix and not something like "DVD by mail." So we had a slight advantage that we didn't fall in love with our first business. We knew it was a path to something else.

On timing. We knew it was an issue. We went public in 2002, and we said eventually internet delivery would come. We didn't do much with it until 2005, when we saw YouTube. That's when we realized: it's beginning. And that's when we started that effort and launched in 2007. As Clay described, the key thing is not getting into the new business. Lots of companies do that. We knew the key was focusing on it—how this is going to grow into it's own business. But it was too young to do that. It didn't have enough content so we couldn't sell a streaming content service on its own. So we "hybridized" it with DVD. As usage and content grew, we knew we could split them apart. That went until 2010. We did our first test of the streaming-only service in Canada—a new country that wasn't used to our DVD service. We wanted to understand: with the content we had, could we build a service that had word-of-mouth and could grow. It was a rocket ship. In our first three days, we got as many subscribers as we thought we'd have in three months. It was clear you could position Netflix as streaming-only.

On "Innovator's Dilemma" dynamics (old business v new). One of the most painful moments on that journey was that DVD business was all the revenue and profit. We were hybridizing it with streaming, and we were getting more streaming attention (and executives). But DVD was getting more attention. So we realized we had to kick out DVD executives from the main management meeting. They weren't adding value in streaming discussion. We always compared ourselves to a "streaming pure play"—what would we do? [Key is to separate them: Apple did the same thing, just in reverse.]

Consider Marc Andreessen and A16Z. In the podcast, "Software programs the world," Andreessen talks about the falling costs of chips:

Let me go to the foundations. Moore's Law has flipped. This has happened over the last 7-8 years. For many years, Moore's Law was a process of the chip industry bringing out a new chip every 1.5 years that was twice as fast as the previous chip at the same price. That continued for 40-50 years. That resulted in everything from mainframes to PCs to smartphones. About 7-8 years ago, that process topped out at 3 GHz. Some people said progress was going to stall. I think what has happened is that Moore's Law has flipped. The dynamic now is instead of increased performance is reduced cost. You now have this dynamic where every 1 to 1.5 years, the chip companies release a chip that is the same speed but half the cost. This is a massive deflationary force in the technology force, and I suspect in the economy in general. Basically, computing is becoming free. What we do in this business is we chart out the graphs and assume that we get to the end point. So what we assume is that chips are going to be free. Which means chips will be embedded in everything. And we've never lived in that sort of world.

On enabling technologies: bandwidth, browsers, and devices. Video had come and gone—for example, with RealPlayer—but never became a huge hit. So what changed that made YouTube possible? At Sequoia, we'd investigated related ideas back in 2004 and 2005. We were keeping an eye on broadband penetration in the US—where was the topping point at which a large enough percentage of US consumers had decent home internet connections? And what new services would that unlock? ... We also kept tabs on the state of browser technology. ... We were listening to semiconductor companies that made the components for handheld devices—devices that made it easy for consumers to capture pictures and videos.

On consumer behavior. There was the emergence of blogging, photo-sharings services like Flickr, and review sites. People wanted to express themselves through text and pictures; the next natural step was video.

On the value proposition and product. When I first encountered the website, I uploaded a few videos. In just a few minutes I'd posted them and e-mailed them out. People were watching videos that had been sitting on my hard drive for years. Other video sites at the time had clients that you had to download. Even with the browser-based ones, their products just weren't as good.

On founder quality.I was lucky enough to know the founders from PayPal. I knew how good they were. And they were fantastic talent magnets. When Google acquired YouTube there were only fifty-five employees.

And also... We have a predisposition toward the long view. If you were to hold onto the shares of every IPO company we invested in until today, you would have made significantly more money than if you were to invest with us at the venture stage alone. ... People overestimate the impact of technological shifts in the short run and underestimate them in the long run. But the long-run effects are just so spectacular.

In each of the cases, there were fundamental underlying shifts, or clear trends indicating a tipping point. The second-order effect of a key (or in some cases, many) underlying shifts—chip speeds, chip costs, bandwidth, browser technology, handheld capabilities, consumer behavior, etc.—enabled something new and valuable.

I came across this on Farnam Street, and it really resonated. Seneca said (emphasis mine):

The primary indication, to my thinking, of a well-ordered mind is a man’s ability to remain in one place and linger in his own company. Be careful, however, lest this reading of many authors and books of every sort may tend to make you discursive and unsteady. You must linger among a limited number of master-thinkers, and digest their works, if you would derive ideas which shall win firm hold in your mind. Everywhere means nowhere. When a person spends all his time in foreign travel, he ends by having many acquaintances, but no friends. And the same thing must hold true of men who seek intimate acquaintance with no single author, but visit them all in a hasty and hurried manner. Food does no good and is not assimilated into the body if it leaves the stomach as soon as it is eaten; nothing hinders a cure so much as frequent change of medicine; no wound will heal when one salve is tried after another; a plant which is often moved can never grow strong. There is nothing so efficacious that it can be helpful while it is being shifted about. And in reading of many books is distraction.

I keep coming back to the below passage from the essay "The Child and the Shadow" in Ursula Le Guin's book, The Language of the Night: Essays on Fantasy and Science Fiction. The essay is an exploration of how fantasy uses symbols and archetypes to communicate with our unconscious: "The great fantasies, myths, and tales are indeed like dreams: they speak from the unconscious to the unconscious in the language of unconscious—symbols and archetype." In the passage below, Le Guin is introducing the reader to Carl Jung's ideas, whom she describes as "the psychologist whose ideas on art are the most meaningful to artists."

Jung's terminology is notoriously difficult, as he kept changing meanings the way a growing tree changes leaves. I will try to define a few of the key terms in an amateurish way without totally misrepresenting them. Very roughly, then, Jung saw the ego, what we usually call the self, as only a part of the Self, the part of it which we are consciously aware of. The ego "revolves around the Self as the earth around the Sun," he says. The Self is transcendent, much larger than the ego; it is not a private possession, but collective—that is, we share it with all other human beings, and perhaps with all beings. It may indeed be our link with what is called God. Now this sounds mystical, and it is, but it's also exact and practical. All Jung is saying is that we are fundamentally alike; we all have the same general tendencies and configurations in our psyche, just as we all have the same general kind of lungs and bones in our body. Human being all look roughly alike; they also think and feel alike. And they are all part of the universe.

The ego, the little private individual consciousness, knows this, and it knows that if it's not to be trapped in the hopeless silence of autism it must identify with something outside itself, beyond itself, larger than itself. If it's weak, or if it's offered nothing better, what it does is identify with the "collective consciousness." That is Jung's term for the lowest common denominator of all the little egos added together, the mass mind, which consists of such things as cults, creeds, fads, fashions, status-seeking, conventions, received beliefs, advertising, popcult, all the isms, all the ideologies, all the hollow forms of communication and "togetherness" that lack real communion or real sharing. The ego, accepting these empty forms, becomes a member of the "lonely crowd." To avoid this, to attain real community, it must turn inward, away from the crowd, to the source: it must identify with its own deeper regions, the great unexplored regions of the Self. These regions of the psyche Jung calls the "collective unconscious," and it is in them, where we all meet, that he sees the source of true community; of felt religion; of art, grace, spontaneity, and love.

This element of turning inward to connect with humanity rather than outward is compelling. Frankly, I don't know if it's true yet, but I find it fascinating, and I'm curious to learn more. So I ordered Carl Jung's book The Undiscovered Self.

I used to be a bit embarrassed about the books I owned but had not yet read (or hadn't finished) until I came across the concept of the antilibrary in Nassim Taleb's book The Black Swan:

The writer Umberto Eco belongs to that small class of scholars who are encyclopedic, insightful, and nondull. He is the owner of a large personal library (containing thirty thousand books), and separates visitors into two categories: those who react with “Wow! Signore professore dottore Eco, what a library you have! How many of these books have you read?” and the others — a very small minority — who get the point that a private library is not an ego-boosting appendage but a research tool. Read books are far less valuable than unread ones. The library should contain as much of what you do not know as your financial means, mortgage rates, and the currently tight real-estate market allows you to put there. You will accumulate more knowledge and more books as you grow older, and the growing number of unread books on the shelves will look at you menacingly. Indeed, the more you know, the larger the rows of unread books. Let us call this collection of unread books an antilibrary.

I only discovered Ursula Le Guin’s writings after she passed recently, and I've found her views to be incredibly rich and inspiring—what an amazing person.

This interview is worth a read, highlighting the breadth and depth of her intellect.

If you need an introduction:

Named a Living Legend by the Library of Congress for her contributions to America’s cultural heritage—the author of more than sixty books of fiction, poetry, creative nonfiction, children’s literature, drama, criticism, and translation—she was one of only a select few writers (the others being Eudora Welty, Saul Bellow, and Philip Roth) to have their life’s work enshrined in the Library of America while still actively writing. She joined the likes of Toni Morrison, John Ashbery, and Joan Didion in receiving the Medal for Distinguished Contribution to American Letters by the National Book Foundation, and her work garnered countless awards: the National Book Award, the PEN/Malamud, six Nebulas, six Hugos, and twenty-one Locus awards among them. Her name regularly appeared on the Nobel Prize for Literature short list, and writers as varied as Neil Gaiman, Salman Rushdie, David Mitchell, and Zadie Smith herald her as an influence.

On imagination and justice:

As Ursula once said in an essay accompanying the 500-year-anniversary edition of Thomas More’s Utopia: “We will not know our own injustice if we cannot imagine justice. We will not be free if we do not imagine freedom. We cannot demand that anyone try to attain justice and freedom who has not had a chance to imagine them as attainable.”

On her distinctive approach to her craft:

I hear what I write. I started writing poetry when I was really young. I always heard it in my head. I realized that a lot of people who write about writing don’t seem to hear it, don’t listen to it, their perception is more theoretical and intellectual. But if it’s happening in your body, if you are hearing what you write, then you can listen for the right cadence, which will help the sentence run clear. And what young writers always talk about—“finding your voice”—well, you can’t find your own voice if you aren’t listening for it. The sound of your writing is an essential part of what it’s doing. Our teaching of writing tends to ignore it, except maybe in poetry. And so we get prose that goes clunk, clunk, clunk. And we don’t know what’s wrong with it.

On battle metaphors:

I do try to avoid saying “the fight” for such and such, “the war” against such and such. I resist putting everything into terms of conflict and immediate violent resolution. I don’t think that existence works that way. I’m trying to remember what Lao Tzu says about conflict. He limits it to the battlefield, where it belongs. To limit all human behavior to conflict is to leave out vast, rich areas of human experience.

I got a lot out of this podcast! I initially listened to it driving, but the insights were so interesting that I had to listen to it twice afterwards, taking notes to absorb it all. It's an exciting brave new world we have coming.

Platforms will be different than what we've had until 5-10 years ago: the platform was a new chip (faster) and a new OS

Platforms today: distributed systems, scale out systems

These are not on a chip, rather built across a lot of chips (distributed systems)

Cloud was first example (AWS) - can now create a program that can run across 20k computers (run for 1h, cost $50)

Rise of Hadoop, Spark (distributed processing)

Financial technology: bitcoin, cryptocurrency

Now: AI (machine learning, deep learning) which is "inherently parallelizable" - can run across many chips and get very powerful as they do so

"Can do things in AI with distributed computing that you couldn’t imagine 5y ago"

The GPU

Initially developed for gaming for very high resolution graphical processing → unexpected uses

"New application of an old idea"

Thirty years ag in physics lab -- if you need a simulatio with large number of parallel calculations (e.g., black holes, biological simulations), write algorithms to parcel problem into pieces and run in parallel

NVIDIA has become “seemingly overnight” → market leader in GPUs and chips for AI

All entrepreneurs in AI building on NVIDIA chips (in contrast to Intel in previous years)

In AI, what are the things that lend themselves well to startups versus larger companies (e.g., FB, Google, Apple)

Challenge: people think of AI as narrower than it really is; rather, it is an entirely new way to write a computer program (broadly applicable to problems)

Could use AI to analyze consumer data (hard to compete with Google)

BUT: many areas where no one has any data yet (HC, autonomy)

Big company advantage: lots of data

In reality: just a new way to write a program

Interfaces

The smartphone was an advance over WIMP

WIMP interface: windows, icons, menus, printer

That was an advance over text based interface of DOS

BUT -- life is different

Natural interfaces: natural language

AI can enable natural language and natural gestures

Opportunity to build interfaces for things you couldn’t before

One idea: what applications couldn’t you have before because there wasn’t a workable use interface for it

Amazon/Alexa

Not tied to old generations

No “strategy tax”

Ability to leapfrog

For many major new advances, interfaces depend on platform

But - big companies also have strategy tax -- existing agenda, have to fit next thing into old platform

Example: Amazon has taken lead from Apple, Google -- even though it flopped with phone!

Lack of phone became an advantage! Clean breakthrough product

But where can startups play?

A year ago, would have said AI would be domain of big companies (can afford engineers, hardware; data sets)

All three have changed

AI technology is standardizing (open source → cloud)

AI as a service

“AWS for AI” (Google, Amazon, Microsoft, etc.)

TensorFlow ("This is a big deal")

A lot of students on TensorFlow (“trickling down very fast”)

Most teams at hackathon had AI and machine learning components

Hardware costs coming down across the board

In one year -- AI supercomputing chips with algorithms in the cloud (massive deflation)

Big data sets -- startups can assemble big data sets BUT...

Newest generation of experts -- focusing on small data sets

"They'll say, Primitive and crude machine learning required large data sets but not the newer algorithms (they can work on small data sets) - early but enticing (brings problems into small company realm)

With these GPUs — can create simulated versions of the real world using video game tools (can train AI)

Earthquakes, floods, thunderstorms, swarms of birds

Train AI - "AI actually has no idea it’s working in a simulated world and not the real world"

Potentially: run millions of hours of simulated training at very low cost

Theme: tech reaching into new places; “tech is outgrowing the tech industry”

Thesis: software is eating the world BUT “hard investments” (Soylent, Oculus, Nutribox)

Oculus was actually software (breakthrough tech often needs new hardware)

Soylent and Nutribox -- same thing

Big believers: big breakthroughs in knowledge (Turing, Shannon) -- new model of the world, companies that build on that new knowledge

SaaS — acquisitions; what is left to do there? SFDC or vertical or totally new platforms

SaaS as old versions of things in the cloud (WDAY, SFDC, SFSF) -- big categories

Changed from on-premise to cloud: seeings sw applications for things that in the old days were cost prohibitive (“screwing it in and hiring army of Accenture consultants) (e.g., expense reporting: Concur) -- new things come into economic viability

What was unviable before?

Can also scale down to small companies as buyers (<1k employees) - Oracle Financials v. NetSuite

“World has never been more ripe for a VERY large wave of innovation that would be quite easy to finance”

More money than ideas and creative, effective people

Company building and founders — types of founders; what has changed?

“Gotten more risk tolerant”

“We’re much more interested in the magnitude of the strengths than the number of weaknesses”

Lack of experience is a strength: “Hard to rewrite the world if you’re too steeped in the world”

Financial terms: “buying volatility”

“World class strengths where we care about them”

One piece of advice

Management: “The most common mistake founders is making decisions based on very proximate perspectives without taking the time to think about how others in the company will see the decision...let’s look past the person I’m talking to.”

Strategic: “People need to raise prices.”

Most companies have sophisticated views on product, design, engineering and naive views on prosecuting a campaign

When you charge higher prices, people take the product more seriously, impute more value, make a serious decision, and when they buy it, they experience a greater sense of engagement, commitment, and stickiness

Where the mind is without fear and the head is held high;Where knowledge is free;Where the world has not been broken into fragments by narrow domestic walls; ...Where the clear stream of reason has not lost its way into the dreary desert sands of habit; ...Into that heaven of freedom, my Father, let my country awake.

The Everything Store by Brad Stone is a tremendously good book. Below are my notes and thoughts.

Bezos on what makes Amazon unique:

At Amazon...

We are genuinely customer-centric

We are genuinely long-term oriented

We genuinely like to invent

“Very few companies have all three of those elements.”

On point of view, or thinking differently:

Alan Kay: “Point of view is worth 80 IQ points.” Examples in the book of unique points of view:

When Amazon launched book reviews, he received an angry letter from a publisher telling him his business was to sell books, not trash them. “We saw it very differently,” Bezos said. “When I read that letter, I thought, we don’t make money when we sell things. We make money when we help customers make purchase decisions.”

D. E. Shaw, where Bezos worked before starting Amazon: “While the rest of Wall Street saw D. E. Shaw as a highly secretive hedge fund, the firm viewed itself differently … [as a] versatile technology laboratory full of innovators and talented engineers who could apply computer science to a variety of different problems. Investing was only the first domain where it would apply its skills.”

Bezos "stealing" ideas:

“I don’t think there was anybody Jeff knew that he didn’t walk away from with whatever lessons he could.”

“Good artists copy, great artists steal” (Picasso).

"Stealing" ideas from Jim Sinegal, CEO of Costco

Sinegal “didn’t have an exit strategy” – “he was building the company for the long term.”

“It was all about customer loyalty.”

“Costco buys in bulk and marks up everything everything at a standard, across-the-board 14 percent, even when it could charge more. It doesn’t advertise at all, and earns most of its gross profit from the annual membership fees.”

Sinegal doesn’t regret educating Bezos: “I’ve always had the opinion that we have shamelessly stolen any good ideas."

Stone doesn’t make the connection, but Prime looks a lot like Costco’s membership fee.

Competition:

At the outset: “There was competition already. It wasn’t as if Jeff was coming up with something completely new.” At least not at first, but he was thinking about it very differently than the others.

When Barnes & Noble launched a competing website and sued Amazon, there was a highly publicized Forrester Research report in which Amazon was referred to as “Amazon.Toast. "Jeff to employees: “Look, you should wake up worried, terrified every morning. But don’t worry about our competitors because they’re never going to send us money anyway. Let’s be worried about our customers and stay heads-down focused.”

Bad news? Assemble the SWAT team:

In early 1998, Mark Breier, the VP of Marketing, showed Bezos a survey that the majority of consumers did not use Amazon.com and were unlikely to do so because they bought very few books. Bezos instructed Breier to assemble a “SWAT team” of recent hires from Harvard Business School to research categories that had high SKUs, were underrepresented in physical stores, and could be easily mailed. Breier: “I brought him very bad news, and for some reason he got excited.” Bezos had the playbook in his head from the beginning. Breier seemed to have nudged him to the next phase.

On Marketing:

“Over the first decade at Amazon, marketing VPs were the equivalent of the doomed drummers in the satirical band Spinal Tap; Bezos plowed through them at a rapid clip, looking for someone with the same low regard for the usual way of doing things that Bezos himself had.”

“We spend only forty basis points on marketing.”

Athletes: On hiring Harrison Miller to lead the rollout of a new category—toys: “Miller knew nothing about toy retailing, but in a pattern that would recur over and over, Bezos didn’t care. He was looking for versatile managers—he called them ‘athletes’—who could move fast and get big things done.”

Articulating culture in 1998: customer obsession, frugality, bias for action, ownership, and high bar for talent.

Coordination: As Amazon grew, coordination became more difficult. At an offsite, a group presented ideas to improve communication between groups. Jeff stood up with a red face and the infamous blood vessel in his forehead pulsing and said, “I understand what you are saying, but you are completely wrong. Communication is a sign of dysfunction. It means people aren’t working together in a close, organic way. We should be trying to figure out ways for teams to communicate less with each other, not more.” The right question wasn’t, How do we communicate better? It was: How do we improve effectiveness. He later said, “A hierarchy isn’t responsive enough to change. I’m still trying to get people to do occasionally what I ask. If I was successful, maybe we wouldn’t have the right kind of company.”

The Innovator's Dilemma as a manual. The Innovator’s Dilemma had a significant impact on Jeff Bezos (and, being an avid reader, many other books did as well).

Steve Kessel ran the book category for a few years until about 2004, when Bezos asked him to take over the emerging digital business.

Bezos: “If you are running both businesses you will never go after the digital opportunity with tenacity.”

Bezos had learned that he needed to set up a new and independent business to pursue a disruptive technology properly

He told Kessel: “Your job is to kill your own business.”

Bezos was influenced by the book Creation by Steve Grand in which Grand described his approach to a 1990s video game called Creatures. Creatures gave players the ability to “guide and nurture a seemingly intelligent organism on their computer screens.” His approach was to allow complex, higher-level behaviors to emerge from simple computational blocks called primitives.

Bezos: “Developers are alchemists and our job is to do everything we can to get them to do their alchemy.”

Can this apply to businesses as well? What if within a business the functions—Product, Marketing, Sales, etc.—were primitives on top of which young, relatively untested leaders built new, experimental businesses?

How many large, successful businesses emerged unexpectedly as the result of solving an internal problem? Palantir is one example.

Gut calls, intuition, vision. One recurring theme in the book is Bezos’s “gut calls”—times when data wasn’t available, was inconclusive, or even pointed to a conclusion contrary to what Bezos believed and Bezos proceeded in line with his intuitions anyway. A few examples:

Prime. “In many ways, the introduction of Amazon Prime was an act of faith.”

“The service was expensive to run, and there was no clear way to break even.”

Diego Piacentini, a senior executive running international operations, said, “We made this decision even though every single financial analysis said we were completely crazy to give two-day shipping for free.”

Bezos, however, knew from “gut and experience” that it had the potential to change customer behavior—and the overall company—dramatically. He had seen Super Saver Shipping lead to bigger orders and purchases in new categories. He had seen the increase in spending due to lower friction from 1-Click ordering.

And Bezos was right: “The service turned customers into Amazon addicts.”

And costs did come into line. The fulfillment organization “got better at combining multiple items from a customer’s order into a single box, which saved money and helped drive down Amazon’s transportation costs by double digit percentages a year.”

This led me to recall this quote from the philosopher Albert Hirschman (source): “Creativity always comes as a surprise to us; therefore we can never count on it and we dare not believe in it until it has happened. In other words, we would not consciously engage upon tasks whose success clearly requires that creativity be forthcoming. Hence, the only way in which we can bring our creative resources fully into play is by misjudging the nature of the task, by presenting it to ourselves as more routine, simple, undemanding of genuine creativity than it will turn out to be.”

AWS and pricing. The AWS team, having some sense of Bezos’s philosophies, initially proposed EC2 pricing at $0.15 an hour at which they would breakeven. Bezos unilaterally changed that $0.10.

Bezos believed Amazon had a natural costs advantage and that at such pricing IBM, Microsoft, Google, etc. would hesitate to enter the market.

Stone doesn’t mention this, but I wonder if it was also another platform vision, another flywheel. Perhaps Bezos saw that compute infrastructure would become another flywheel connected to the distribution infrastructure flywheel. Increased usage of the distribution infrastructure flywheel led to lower prices, whereas increased usage of the compute infrastructure flywheel led to product innovation.

Kindle pricing. Bezos priced the books at $9.99. “There was no research behind that number—it was Bezos’s gut call.” The price for digital books was the same as that for physical books, typically $15, so it meant they would lose money, but Bezos believed that publishers would eventually lower their prices on digital books to reflect their lower costs.

Kindle wireless. Wireless connectivity to a cellular connection had never been tried before, but Bezos believed that consumers should be able to download a book easily without having to connect to wifi. Bezos faced resistance on both the engineering and the economics but pushed them to do it anyway.

Random customer anecdotes. “Random customer anecdotes, the opposite of cold, hard data, also carry tremendous weight and can change Amazon policy. If one customer had a bad experience, Bezos often assumes it reflects a larger problem and escalates the resolution of the matter inside his company with a question mark.” Wilke: “It’s an audit that is done for us by our customers. We treat them as precious sources of information.”

This is why Medallia is an incredible product and on track to be an incredible business.

Distribution centers. “[Amazon’s accounting group] fretted about opening seven costly distribution centers and even about having gotten so deeply immersed in the muck of distribution in the first place. Bezos insisted the company needed to master anything that touched the hallowed customer experience, and he resisted efforts to project profitability. ‘If you are planning for more than twenty minutes ahead in this kind of environment, you are wasting your time,' he said in meetings.”

My wife, Neval, and I celebrated our sixth anniversary a few weeks ago and did something new: we went to the San Francisco Symphony.

Garrick Ohlsson played Rachmaninoff's Third Piano Concerto, which we enjoyed. For the encore, however, Ohlsson announced the piece as something "that is so famous it needs no introduction" and went on to play a breathtakingly beautiful, elegant piece.

We actually didn't recognize the piece, but @sfsymphony helped out:

@sfsymphony Can you please tell me the name of the piece Ohlsson played in his encore performance last night (Sat)?

It is commonly thought that the most usual conservatives are the old, and the innovators are young people. That is not quite correct. The most usual conservatives are young people. Young people who want to live, but who do not think and have no time to think about how one should live, and who therefore choose as a model for themselves the life that was.

Apparently, the three Chinese internet congolomerates, Baidu, Alibaba, and Tencent, are referred to as BAT, and Alibaba was a wake up call to the world.

For the twelve month period ending June 2014, this was Alibaba's performance:

Revenue: $9.3 billion

Revenue growth rate: 92 percent

ROIC: 97 percent

Pre-tax operating margins: 50 percent

Aswath Damodaran gave Alibaba an equity valuation of $160 billion, or $66 per share. And, by the way, he assumed the 92 percent annual revenue growth dropped to 25 percent for the next five years and tapered to 2.41 percent, the U.S. risk free rate, by year ten—a pretty significant assumption given Alibaba's recent growth, immense market leadership, and relatively low penetration of the Chinese market. Alibaba is currently trading at $88, or about a $215 billion market cap.

Stepping back, Mike Moritz had some interesting things to say in the WSJ:

What has been apparent to very close observers for several years and people who were interested in investing in China is that the whole global online chess board is being rearranged, and Alibaba is the latest and most profound example of that.

Over the next decade, what has effectively been separate theaters of activity — China and the West — will become one global battlefield.

People in the U.S. and Europe are probably in a state of suspended denial about the ambition of the four or five leading Chinese companies. If you do a stack ranking of the most valuable Internet companies in the world and you throw in Google, Facebook, Alibaba, eBay, Tencent, Baidu, Naspers — it isn’t an overwhelming percentage that is American.

Over the next decade, to some extent, I think the advantage lies with the Chinese companies. The Chinese companies will have an easier time competing in the West then the Western companies will have competing in China.

The following are slightly dated, from The Financial Times in March 2014 (so the Alibaba market cap is off), but a good summary of the global internet landscape nonetheless:

Some of the noteworthy insights in that article:

They're disrupting the status quo.

Their expansion highlights rapid evolutions in the Chinese internet market, a source of wealth and power that challenges the country’s traditionally state-led economy.

Two factors have shaken up the cosy world of China’s internet. The first is that internet companies have become the largest private-sector companies in China by capitalisation and revenues and are awash with cash. Second, the arrival of the mobile internet has given them something to fight over. Nearly half a billion Chinese use smartphones that cost as little as $50 each to get online.

Mobile is forcing them to adjust.

“Before, each of these companies had a distinct sphere, but with the arrival of mobile internet there is more and more convergence on a single model, and more areas of overlap. That’s where the battle lines are now,” says Arthur Kroeber of Gavekal Dragonomics, the research group.

Most of the new acquisitions have been made with mobile in mind. Baidu paid $1.9bn in July for 91 Wireless, an app store designed to give it an edge with mobile users. Alibaba has an undisclosed stake in UC Web, China’s top mobile browser. Last month Alibaba made an offer for AutoNavi Holdings, a mapping service, valuing it at $1.6bn, and allowing it to compete head-to-head with Baidu’s mobile map app.

Conglomerates are emerging (and not just in China; Google, Apple, Facebook, and Amazon in the U.S.)

China’s internet giants are becoming what analyst Anne Stevenson-Yang of J Capital Research calls “tech Kereitsus”, referring to the national champions that dominated the Japanese economy in the 20th century with interests in multiple industries. “When companies are this big in China, the difference between public and private is not that important,” she says. “For all intents and purposes these companies have become the ministry of the internet.”

Tren Griffin pointed out that Yuri Milner at DST was able to connect the dots on Facebook's future better than many others because he was able to look at models in Russia. He called it "information arbitrage". I read something similar about Roelof Botha looking at Korea and Japan for indicators that the U.S was ready for various new offerings and how this framework was an important factor in his YouTube investment.

Another fascinating read, also touching on the idea of information arbitrage, was the FT's interview with Zhang Lei of Hillhouse Capital, an early investor in Tencent.

Zhang holds informal Hillhouse gatherings with the leaders of private companies, many of them the consumer and tech enterprises in which Hillhouse invests, and many of them on the verge of going public. “The entrepreneurs in my portfolio companies learn from each other,” Zhang says, noting that he has fostered study sessions between JD and a hypermarket chain he has invested in. “Etailers learn how offline companies think and retailers learn how ecommerce companies think.”

He cites a practical example of companies learning from each other: Zhang invested in Blue Moon, a liquid detergent maker, and had its executives meet JD. That session led Blue Moon to redesign its liquid detergent refill packs so they could fit more easily into JD’s delivery bins. “Bulky is an advantage to attract consumers in a physical world but it is a disadvantage in a virtual world,” he says.

Now Zhang is taking the Chinese template offshore. “The Chinese model, which is mobile-driven, is more suited to emerging markets than the US model, which is desktop driven,” he says. “The socio-economic profile is more similar. We can help companies like Tencent go abroad and accelerate the growth of the mobile internet elsewhere and others also can leapfrog. It is a win-win situation. We are changing intra-Asian trade.”

In Indonesia, for example, Zhang created a joint venture between Tencent’s WeChat mobile messaging platform and Global Mediacom, Indonesia’s largest media, television and pay TV conglomerate. “Indonesia now is like China some years ago,” he says.

There's a lot to think about here, but the biggest takeaway for me from all this is that while Silicon Valley retains a lead in the global technology marketplace, they dynamic is shifting in an unexpected yet interesting way. At least studying in far greater detail what Baidu, Alibaba, and Tencent are doing in the context of their environment, if not spending a significant amount of time in China to truly understand the dynamics, will yield tremendous returns for the ecosystem in Silicon Valley. It's hard to break out of the Valley mindset at times, and this is one way to do that.

The Everything Store, the story of Amazon by Brad Stone, has a number of interesting stories, one in particular about Bezos’s time at the hedge fund D. E. Shaw.

David Shaw saw the opportunity in the internet early and tapped Bezos to help him investigate. As Stone writes, “Intrigued by Shaw’s conviction about the inevitable importance of the internet, Bezos started researching its growth.”

It was only then that Bezos learned from the February 1994 issue of Matrix News, a monthly newsletter with facts and analysis about the internet, that from January 1993 to January 1994, essentially the first year of the internet, the number of bytes transmitted over the internet had increased by a factor of 2,057. Another fact was that the number of packets had increased by a factor of 2,560. Bezos summarized the two facts to say that the internet had grown by a factor of about 2,300 in its first year. (It's worth noting that Bezos later mistakenly characterized the growth as 2,300%, which while still large, is still off by two orders of magnitude.)

Shaw and Bezos went on to investigate three ideas:

Email. They created a free, advertising-supported email system called Juno, which went public in 1999 and merged with rival NetZero.

Online trading. Shaw created FarSight Financial Services, an early E-Trade, in 1995 and sold it to Merrill Lynch.

The everything store. They also discussed e-commerce, the idea of “an Internet company that served as the intermediary between customers and manufacturers and sold nearly every type of product, all over the world.”

Bezos dived into “the everything store” idea and concluded that such scope would be impractical at first. He listed twenty categories, including software, office supplies, apparel, and music, and concluded that books were the ideal starting point. It was then that Bezos decided to leave D. E. Shaw to pursue the idea.

What I find fascinating about this story is that it’s actually not what common lore about Amazon’s founding leads you to believe. Legend says that Bezos was led down the Amazon path when he saw the 2,300 times growth, when in fact, it was David Shaw that saw the opportunity first. It was conviction first, research and facts later.

Shaw saw the opportunity because of his technology orientation and his framework for D. E. Shaw:

While the rest of Wall Street saw D. E. Shaw as a highly secretive hedge fund, the firm viewed itself somewhat differently. In David’s estimation, the company wasn’t really a hedge fund but a versatile technology laboratory full of innovators and talented engineers who could apply computer science to a variety of different problems. Investing was only the first domain where it would apply its skills.

Framed differently, others had access to the same data in Matrix News that Bezos saw. It was those facts and analysis overlaid on the framework and mindset from Shaw that compelled the idea.

This echoes what I’ve seen elsewhere in early stage companies: conviction emerging from experience and intuition matter more than facts and analysis. In fact, almost by definition with early stage opportunities, the facts and analysis won’t justify the opportunity.

Earlier this year, there was a bit of an uproar when it emerged that Chamath Palihapitiya had sold his 10 percent stake in Tinder to IAC for $500 million, implying a Tinder valuation of $5 billion and a mind-boggling windfall for Chamath.

(Chamath, if you're not familiar, was a key person at Facebook, leading its growth team. He made a bit of cash and became an investor, starting the venture capital firm Social + Capital. And Tinder, of course, is a popular dating app, incubated at the internet company, IAC.)

It turned out the reports weren’t true, and while the exact figure wasn’t known, it was clear the stake had been sold and, while far lower, for a pretty decent sum nonetheless: later reports put his stake at 11 percent and the sale price at $55 million.

As I read more, I was fascinated, and I’m writing the story and my thoughts down now, almost four months after the fact, because I find a lot to admire in what Chamath's moves leading up to that sale imply about Chamath’s thinking.

This is the story:

Tinder was jointly developed by IAC and a Toronto-based mobile development firm called Xtreme Labs. IAC retained control and the lion’s share of equity, with Xtreme Labs retaining, it seems, 11 percent. The app was developed inside a joint venture between the two called Hatch Labs, which was shuttered in late 2013.

Chamath bought a majority stake in Xtreme Labs in late 2012 for $20 million of his own money (i.e., not via his venture firm). The co-founder and CEO of Xtreme Labs, Amar Verma, and Chamath had known each since college, and Chamath had worked with Xtreme on some projects at Facebook.

As detailed by Liz Gannes of All Things Digital:

Palihapitiya told me the deal makes sense in light of the current scarcity of good mobile developers. It will be worth it to him to be able to use Xtreme’s spare time to help with Social+Capital projects, and to spin out interesting start-ups. And Xtreme is now working on open-source frameworks that will bring its native app expertise to a broader audience.

The structure was $6 million up front and $20 million (unclear whether that is inclusive of the $6 million) over the next three years.

The most recent comment in the All Things Digital piece, which about captures the general confusion around the purchase (and that from the small number that cared at all), read:

This is the worst thing I’ve seen an investor do. Are you serious? This is a development shop with low margins. I know this team, and I know this space incredibly well. Just pull out of this deal ASAP or reduce your stake for Jesus sakes. Wow. I had respect for Chamath once. Horrible.

In late 2013, Xtreme Labs was sold to Pivotal Labs (EMC) for $65 million cash plus incentive compensation to the staff of 300 or so. Chamath, however, kept the equity stake in Tinder for himself as part of the deal. Exactly what Chamath earned on the sale is a function of how much of the total $20 million committed he ended up investing and what exactly “majority stake” means, but regardless it’s a decent multiple. Let’s say it was $20 million for 80 percent. The $65 million sale would have netted him $52 million for a 2.6 return with a holding period of about a year.

Then, about six months later, Chamath sold the stake in Tinder for $55 million if we believe the reports. The $52 million from Xtreme plus $55 million for Tinder yields $107 million for a 5.4x return in about a year and half. Not bad.

This is what I think is noteworthy:

Forget the economic return, though that alone is noteworthy.

When everyone else, including Chamath himself via his venture firm, was investing in applications, Chamath bought a development firm.

My guess is that, having looked at a large sample of companies and given his own experience, he did the back of the envelope math on number of possible opportunities and the scarcity of good development teams. He referenced the "scarcity of good mobile developers" thesis to Gannes, but I’d word it differently: there’s a scarcity of good development engines, groups that can work together to put out good product.

A slightly different lens is that there even fewer good mobile development engines with great optionality. Yes, good teams come together and start companies in which case the thesis is defined and the initial direction largely set. Good teams leave themselves open to insight and groups do "pivot" so often there is a lot of optionality, but there aren’t many truly experimental development engines. I believe Chamath saw Xtreme Labs as a way to learn and experiment, to engage in "black swan farming" (stealing Paul Graham's phrase). He ended up selling and netting a great outcome, but there’s an argument that he may have sold too soon.

What I like most about this story is the unconventional, first-principles thinking. If you're investing in ideas around mobile and being thoughtful about the macro trends, comparing the supply of talent with the demand for (and dramatic upside in) offerings, buying a development firm is a natural outcome of that logic. Unlike others though, Chamath was willing to follow the logic of his analysis all the way into core development talent. He was investing in teams and companies via his venture firms, but he was also building core capabilities and learning via the investment in Xtreme Labs.

Clarification: Beyond the facts, I don't know if any of this is true—all conjecture.

We are still the masters of our fate. Rational thinking, even assisted by any conceivable electronic computors, cannot predict the future. All it can do is to map out the probability space as it appears at the present and which will be different tomorrow when one of the infinity of possible states will have materialized. Technological and social inventions are broadening this probability space all the time; it is now incomparably larger than it was before the industrial revolution—for good or for evil.

The future cannot be predicted, but futures can be invented. It was man’s ability to invent which has made human society what it is. The mental processes of inventions are still mysterious. They are rational but not logical, that is to say, not deductive.

Dr. Simons received his doctorate at 23; advanced code breaking for the National Security Agency at 26; led a university math department at 30; won geometry’s top prize at 37; founded Renaissance Technologies, one of the world’s most successful hedge funds, at 44; and began setting up charitable foundations at 56.

In the video below, Simons shares some guiding principles with MIT students. I liked one piece of wisdom in particular:

Be guided by beauty. Everything I’ve done has had an aesthetic component to me. Building a company trading bonds…what’s aesthetic? If you’re the first one to do it right, it’s a terrific feeling and a beautiful thing to do something right, like solving a math problem.

My other notes and takeaways from the talk:

Be first: do something no one else is doing.

Powerful insights on data everyone else has = success. Powerful insights on data no one else has = tremendous success. As Simons recounts:

In those days, we sent people down to the NY Federal Reserve to copy histories of interest rate numbers. They didn’t exist in the ‘70s. You couldn’t buy data, and there certainly wasn’t online delivery. To build the original models, you had to collect a lot of data by hand, which we did.

"What’s the secret to our success?"

People. "We start with great scientists. We start with first class people that have done first class work, or that we have reason to believe will do first class work. Because I was there at the beginning with a few other people that were pretty good at math and science, we had pretty good taste."

Infrastructure. “We provide people with a great infrastructure. It’s easier to get to work here than anywhere else.”

Open environment. “The most important thing we do is have an open atmosphere. My belief is that the best way to conduct research on a broad scale is to make sure as much as possible that everyone knows what everybody else is doing. (At least as quickly as possible. Sometimes you want to keep an idea to yourself for a bit so you don’t look like an idiot.) The sooner the better, start talking to other people about what you’re doing. Because that’s what will stimulate things the fastest. No compartmentalization. Everybody meets once a week. Any new idea gets brought up, discussed, vetted, and hopefully put into production. It’s an open atmosphere.”

Alignment to firm success. “And people get paid on the overall profits, not on their own work. Everyone has an interest in everyone else’s success.”

"Those policies—no one of which seems remarkable—turn out to be a pretty winning combination: great people, great infrastructure, open environment, and try to get everyone compensated roughly based on overall performance."

"My guiding principles"

Different. “Do something new. I love to do something new. I don’t like to run with the pack. For one thing, I’m not such a fast runner. If you’re one of n people working on the same problem in different places, I know if it were me I’d be last. I’m not going to win that race. But if you can think of a new problem or a new way of doing something, that other people aren’t all working on at the same time, maybe that would give you a chance.”

People. "Collaborate with the best people you possibly can. When you see a person, or get to know a person, that seems like a great guy or a great gal to work with at something, try to find a way to do it. Because that gives you some reach and some scope. And it’s also fun to work with terrific people.”

Beauty. "Be guided by beauty. Pretty much everything I’ve done has had an aesthetic component, at least to me. Now you might think, building a company trading bonds—what’s so aesthetic about that? What’s aesthetic about it is doing it right. Getting the right kind of people, approaching the problem, and doing if right. If you feel you’re the first one to do it right—and I think we were—that’s a terrific feeling. It’s a beautiful thing to do something right. It’s also a beautiful thing to solve a mathematics problem or create some mathematics that people hadn’t thought of before.

Persistence. "Don’t give up. At least, try not to give up. Sometimes it’s appropriate to be at something, trying to do something, for a hell of a long time."

[We] to try more to profit from always remembering the obvious than from grasping the esoteric. … It is remarkable how much long-term advantage people like us have gotten by trying to be consistently not stupid, instead of trying to be very intelligent. There must be some wisdom in the folk saying, `It’s the strong swimmers who drown.’

I’m slowly making my way through Clay Christensen’s latest piece in HBR, "The Capitalist Dilemma," which seeks to understand why “despite historically low interest rates, corporations are sitting on massive amounts of cash and failing to invest in innovations that might foster growth.”

Christensen, along with co-author Derek van Bevers, lays out a nuanced argument, detailing three different types of innovations—performance-improving, efficiency, and market-creating—and argues that various incentives have combined to drive “companies [to] invest primarily in efficiency innovations, which eliminate jobs, rather than market-creating innovations, which generate them.”

Christensen and van Bevers explore the reasons driving this shift and also lay out four proposed solutions. One called “Rebalancing Business Schools” caught my eye because it echoes something I’ve been thinking about and am starting to believe is a significant shortcoming in how we think about businesses.

From that section:

Much as it pains us to say it, a lot of the blame for the capitalist’s dilemma rests with our great schools of business, including our own. In mapping the terrain of business and management, we have routinely separated disciplines that can only properly be understood in terms of their interactions with one another, and we’ve advanced success metrics that are at best superficial and at worst harmful.

Finance is taught independently in most business schools. Strategy is taught independently, too—as if strategy could be conceived and implemented without finance. The reality is that finance will eat strategy for breakfast any day—financial logic will overwhelm strategic imperatives—unless we can develop approaches and models that allow each discipline to bring its best attributes to cooperative investment decision making. As long as we continue this siloed approach to the MBA curriculum and experience, our leading business schools run the risk of falling farther and farther behind the needs of sectors our graduates aspire to lead.

The intricate workings of the resource allocation process often are not studied at all in business schools. As a result, MBAs graduate with little sense of how decisions in one part of the enterprise relate to or reflect priorities in other parts. One of our alumni noted, “The only way we learned what projects to invest in was in FIN I [the introductory finance course at HBS].” A whole host of questions goes unasked—and unanswered: How do I identify conditions that signal opportunity for long-term, growth-creating investment? What proxies for estimated future cash flows can I use in evaluating an investment that is pointed toward a new market? How do we identify and build innovations that will help noncustomers perform jobs they need to get done? When are the traditional metrics of IRR and NPV most appropriate, and when are they likely to lead us astray? Since the functions of the enterprise are interdependent, we should mirror this in our teaching.

And lest one think that this is an academic question, removed from reality, Charlie Munger, Vice Chairman of Berkshire Hathaway, articulated a similar idea at the 2011 annual meeting of Berkshire Hathaway:

Costco of course is a business that became the best in the world in its category. And it did it with an extreme meritocracy, and an extreme ethical duty—self-imposed to take all its cost advantages as fast as it could accumulate them and pass them on to the customers. And of course they’ve created ferocious customer loyalty. It’s been a wonderful business to watch—and of course strange things happen when you do that and when you do that long enough. Costco has one store in Korea that will do over $400 million in sales this year. These are figures that can’t exist in retail, but of course they do. So that’s an example of somebody having the right managerial system, the right personnel solution, the right ethics, the right diligence, etcetera, etcetera. And that is quite rare. If once or twice in your lifetime you’re associated with such a business you’re a very lucky person.

The more normal business is a business like, say, General Motors, which became the most successful business of its kind in the world and wiped out its common shareholders… what, last year? That is a very interesting story—and if I were teaching business school I would have Value-Line-type figures that took me through the entire history of General Motors and I would try to relate the changes in the graph and data to what happened in the business. To some extent, they faced a really difficult problem—heavily unionized business, combined with great success, and very tough competitors that came up from Asia and elsewhere in Europe. That is a real problem which of course… to prevent wealth from killing people—your success turning into a disadvantage—is a big problem in business.

And so there are all these wonderful lessons in those graphs. I don’t know why people don’t do it. The graphs don’t even exist that I would use to teach. I can’t imagine anybody being dumb enough not to have the kind of graphs I yearn for. [Laughter] But so far as I know there’s no business school in the country that’s yearning for these graphs. Partly the reason they don’t want it is if you taught a history of business this way, you’d be trampling on the territories of all the professors and sub-disciplines—you’d be stealing some of their best cases. And in bureaucracies, even academic bureaucracies, people protect their own turf. And of course a lot of that happened at General Motors. [Applause]

I really think the world … that’s the way it should be taught. Harvard Business School once taught it much that way—and they stopped. And I’d like to make a case study as to why they stopped. [Laughter] I think I can successfully guess. It’s that the course of history of business trampled on the territory of barons of other disciplines like the baron of marketing, the baron of finance, the baron of whatever.

IBM is an interesting case. There’s just one after another that are just utterly fascinating. I don’t think they’re properly taught at all because nobody wants to do the full sweep.

And that's just part of it: combining business lenses into a holistic view of what it means to build a profitable, sustainable business. An idea I've been toying with is that the act of creating truly unique and valuable businesses draws from the arts and humanities as well, that all great businesses, which are essentially collections of people creating things to sell to other groups of people, "make our hearts sing."